Innovative Method Research Articles

Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis.

This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.

Lithium-Ion Battery Capacity Estimation Based on Incremental Capacity Analysis and Deep Convolutional Neural Network

Accurate estimation of Li-ion battery capacity is critical for a battery management system (BMS). This paper proposes an innovative method which combines a convolutional neural network and incremental capacity analysis (ICA). In the present approach, the voltage and temperature, which significantly affect the ICA curve during the discharging process, are adopted as the inputs for CNN. Rather than extracting feature parameters of an IC curve, as is carried out in the available research, the present method uses the whole ICA curve as the input to avoid complicated feature extraction and correlation analysis. The results show that the maximum error of capacity estimation is less than 4.7%, the rectified mean squared error is less than 1.3% for each battery, and the overall RMSE is below 1.12%.

A novel high-resolution imaging method based on sparsity in the time domain and spectral fitting in the frequency domain

During the propagation of seismic waves underground, the high-frequency seismic response of thin reservoir is absorbed and attenuated, which poses a challenge in seismic thin reservoir prediction. The high-resolution processing techniques have the capability to significantly expand the frequency range of the seismic data, so it becomes a key technique for thin reservoir prediction. Most of these techniques necessitate the extraction of seismic wavelets. However, the spatial and temporal variations of seismic data result in multiple solutions for wavelet extraction. Simultaneously, the majority of techniques fail to consider the influence of spatial tectonic features on the high-resolution processing. In this paper, we propose a novel solution to address these two fundamental challenges by utilizing seismic spectral expansion, sparse reflection coefficients, and spatial continuity constraints. First, we propose an innovative spectral fitting method that aims to expand the frequency bandwidth while adhering to the desired wavelet constraints. This method allows us to fully utilize the effective frequency information. It not only obtains broadband seismic data but also captures precise wavelets. Then, sparse deconvolution is employed to further extend the frequency range by utilizing the accurately expected wavelet and obtaining a high-resolution reflection coefficient. Finally, the Hessian matrix regularization is employed to constrain the spatial continuity of the reflection coefficient. This method is validated in both the model and real seismic data. Compared to traditional sparse deconvolution and spectral modeling deconvolution with spatial constraints, this method not only expands the frequency bandwidth and enhances seismic resolution but also preserves operational frequency information and improves the spatial continuity of seismic data. It has been verified that this approach can be used to forecast thin reservoir and reconstruct spatial tectonic characteristics.

Innovative bio-pyrolytic method for efficient biochar production from maize and pigeonpea stalks and their characterization

Agricultural residues in excess of livestock fodder are garnering global attention and stern concerns owing to their accountable share in environmental hazards due to the lack of effective disposal mechanisms and indiscriminate burning. Recycling these residues for biochar production using pyrolysis is a cost-effective and locally feasible technique which offers a twin-prong solution addressing both climate and soil health issues. This research work compares a portable kiln prototype that is affordable and easy to use, with a muffle furnace at three distinct pyrolytic temperatures (400 °C, 500 °C, and 600 °C) to produce biochar from the stalks of maize and pigeonpea. The biochar properties were characterized using Electron Microscopy-Electron Dispersive X-ray (SEM-EDX), X-ray Diffraction (XRD), Fourier Transmission Infrared Spectroscopy (FTIR), and Thermogravimetric Analysis (TGA). The findings indicate significant variations in biochar properties based on raw material source, pyrolytic method, and varied temperatures. Higher pyrolysis temperatures were found to reduce the amorphous organic phase and alter the ultrastructure of biochar, as evidenced by XRD analysis. SEM imaging showed macropores in oval and round shapes with crystalline deposits. The carbon content, as per EDX, decreased with increasing temperature, aligning with changes in functional groups. Edinburgh's stability test revealed that kiln biochar has more stable carbon content compared to biochar produced using muffle furnace and the stable carbon increased with the rise in temperature. A comparative analysis demonstrated that biochar quality at 400–500 °C in a muffle furnace was on par with that produced in the portable kiln at 400 °C. Therefore, considering the kiln's portability, efficiency, cost-effectiveness, and scalability, it is a promising decentralized method for biochar production, offering a cutting-edge solution for agricultural waste management and soil carbon enhancement.

Can Nano Yield Big Insights? Oligonucleotide-Based Biosensors in Early Diagnosis of Gastric Cancer

Gastric cancer (GC) remains a significant global health challenge, with late-stage diagnosis impacting treatment options and decreased survival rates. To address this, there has been a growing interest in the development of innovative screening and diagnostic methods. Over the past 20 years, nanobiosensors have undergone multiple iterations and unveiled remarkable features that pledge to reshape patient care. Despite the excitement over the plethora of ground-breaking advancements for cancer detection, use-ready samples and streamlined healthcare information monitoring and usage, this technology is still awaiting entry into clinical trials, urging a closer gaze within the medical community. Oligonucleotide-based biosensors, leveraging DNA or RNA’s long-term storage of information, offer great specificity and sensitivity, as described throughout this paper. Consequently, this renders them as an ideal choice for revolutionizing GC diagnosis and facilitating early intervention. The aim of this review is to provide an overview of this cutting-edge, invaluable technology and its limitations across various aspects.

Enhanced Genetic-Algorithm-Driven Triple Barrier Labeling Method and Machine Learning Approach for Pair Trading Strategy in Cryptocurrency Markets

In the dynamic world of finance, the application of Artificial Intelligence (AI) in pair trading strategies is gaining significant interest among scholars. Current AI research largely concentrates on regression analyses of prices or spreads between paired assets for formulating trading strategies. However, AI models typically exhibit less precision in regression tasks compared to classification tasks, presenting a challenge in refining the accuracy of pair trading strategies. In pursuit of high-performance labels to elevate the precision of classification models, this study advanced the Triple Barrier Labeling Method for enhanced compatibility with pair trading strategies. This refinement enables the creation of diverse label sets, each tailored to distinct barrier configurations. Focusing on achieving maximal profit or minimizing the Maximum Drawdown (MDD), Genetic Algorithms (GAs) were employed for the optimization of these labels. After optimization, the labels were classified into two distinct types: High Risk and High Profit (HRHP) and Low Risk and Low Profit (LRLP). These labels then serve as the foundation for training machine learning models, which are designed to predict future trading activities in the cryptocurrency market. Our approach, employing cryptocurrency price data from 9 November 2017 to 31 August 2022 for training and 1 September 2022 to 1 December 2023 for testing, demonstrates a substantial improvement over traditional pair trading strategies. In particular, models trained with HRHP signals realized a 51.42% surge in profitability, while those trained with LRLP signals significantly mitigated risk, marked by a 73.24% reduction in the MDD. This innovative method marks a significant advancement in cryptocurrency pair trading strategies, offering traders a powerful and refined tool for optimizing their trading decisions.

THUBreast: an open-source breast phantom generation software for x-ray imaging and dosimetry

Objective. Establishing realistic phantoms of human anatomy is a continuing concern within virtual clinical trials of breast x-ray imaging. However, little attention has been paid to glandular distribution within these phantoms. The principal objective of this study was to develop breast phantoms considering the clinical glandular distribution. Approach. This research introduces an innovative method for integrating glandular distribution information into breast phantoms. We have developed an open-source software, THUBreast 4 4 http://github.com/true02Hydrogen/THUBreast/ , which generates breast phantoms that accurately replicate both the structural texture and glandular distribution, two crucial elements in breast x-ray imaging and dosimetry. To validate the efficacy of THUBreast, we assembled three groups of breast phantoms (THUBreast, patient-based, homogeneous) for irradiation simulation and calculated the power-law exponents ( β ) and mean glandular dose ( Dg ), indicators of texture realism and radiation risk, respectively, utilizing MC-GPU. Main results. Upon the computation of the Dg for the THUBreast phantoms, it was found to be in agreement with that absorbed by the phantoms based on patients, with an average deviation of 4%. The estimates of average Dg thus obtained were on average 23% less than those computed for the homogeneous phantoms. It was observed that the homogeneous phantoms did overestimate the average Dg by 30% when compared to the phantoms based on patients. The mean value of β for the images of THUBreast phantoms was found to be 2.92 ± 0.08, which shows a commendable agreement with the findings of prior investigations. Significance. It is evidently clear from the results that THUBreast phantoms have a preliminary good performance in both imaging and dosimetry in terms of indicators of texture realism and glandular dose. THUBreast represents a further step towards developing a powerful toolkit for comprehensive evaluation of image quality and radiation risk.

Another Step Forward for Freezing of Gait in Parkinson's Disease.

The study "A spinal cord neuroprosthesis for locomotor deficits due to Parkinson's disease" by Milekovic et al. introduces a novel neuroprosthesis for treating locomotor deficits in late-stage Parkinson's disease (PD). This approach employs an epidural spinal array targeting dorsal roots and electromyography to create a spatiotemporal map of muscle activation, aiming to restore natural gait patterns. Significant improvements in gait freezing and balance were observed in both non-human primate models and a human patient, resulting in improved mobility and quality of life. This innovative method, integrating real-time feedback and non-invasive motor intention decoding, marks a significant advancement in PD treatment.

AbDPP: Target-oriented antibody design with pretraining and prior biological structure knowledge.

Antibodies represent a crucial class of complex protein therapeutics and are essential in the treatment of a wide range of human diseases. Traditional antibody discovery methods, such as hybridoma and phage display technologies, suffer from limitations including inefficiency and a restricted exploration of the immense space of potential antibodies. To overcome these limitations, we propose a novel method for generating antibody sequences using deep learning algorithms called AbDPP (target-oriented antibody design with pretraining and prior biological knowledge). AbDPP integrates a pretrained model for antibodies with biological region information, enabling the effective use of vast antibody sequence data and intricate biological system understanding to generate sequences. To target specific antigens, AbDPP incorporates an antibody property evaluation model, which is further optimized based on evaluation results to generate more focused sequences. The efficacy of AbDPP was assessed through multiple experiments, evaluating its ability to generate amino acids, improve neutralization and binding, maintain sequence consistency, and improve sequence diversity. Results demonstrated that AbDPP outperformed other methods in terms of the performance and quality of generated sequences, showcasing its potential to enhance antibody design and screening efficiency. In summary, this study contributes to the field by offering an innovative deep learning-based method for antibody generation, addressing some limitations of traditional approaches, and underscoring the importance of integrating a specific antibody pretrained model and the biological properties of antibodies in generating novel sequences. The code and documentation underlying this article are freely available at https://github.com/zlfyj/AbDPP.

Understanding the biofilm development of Acinetobacter baumannii and novel strategies to combat infection.

Acinetobacter baumannii (A. baumannii) is a Gram-negative, nonmotile, and aerobic bacillus emerged as a superbug, due to increasing the possibility of infection and accelerating rates of antimicrobial agents. It is recognized as a nosocomial pathogen due to its ability to form biofilms. These biofilms serve as a defensive barrier, increase antibiotic resistance, and make treatment more difficult. As a result, the current situation necessitates the rapid emergence of novel therapeutic approaches to ensure successful treatment outcomes. This review explores the intricate relationship between biofilm formation and antibiotic resistance in A. baumannii, emphasizing the role of key virulence factors and quorum sensing (QS) mechanisms that will lead to infections and facilitate insight into developing innovative method to control A. baumannii infections. Furthermore, the review article looks into promising approaches for preventing biofilm formation on medically important surfaces and potential therapeutic methods for eliminating preformed biofilms, which can address biofilm-associated A. baumannii infections. Modern advances in emerging therapeutic options such as antimicrobial peptide (AMPs), nanoparticles (NPs), bacteriophage therapy, photodynamic therapy (PDT), and other biofilm inhibitors can assist readers understand the current landscape and future prospects for effectively treating A. baumannii biofilm infections.

Acoustic Detection of Vaccine Reactions in Hens for Assessing Anti-Inflammatory Product Efficacy

Acoustic studies on poultry show that chicken vocalizations can be a real-time indicator of the health conditions of the birds and can improve animal welfare and farm management. In this study, hens vaccinated against infectious laryngotracheitis (ILT) were acoustically recorded for 3 days before vaccine administration (pre-reaction period) and also from vaccination onwards, with the first 5 days being identified as the “reaction period” and the 5 following days as “post reaction”. The raw audio was pre-processed to isolate hen calls and the 13 Mel-frequency cepstral coefficients; then, the spectral centroid and the number of vocalizations were extracted to build the acoustic dataset. The experiment was carried out on the same farm but in two different houses. The hens from one house were assigned to the control group, without administration of the anti-inflammatory product, and the other formed the treatment group. Both acoustic data sets were recorded and processed in the same way. The control group was used to acoustically model the animal reaction to the vaccine and we automatically detected the hens’ vaccine reactions and side effects through acoustics. From Scikit-Learn algorithms, Gaussian Naive Bayes was the best performing model, with a balanced accuracy of 80% for modeling the reactions and non-reactions caused by ILT in the control group. Furthermore, the importance of algorithm permutation highlighted that the centroid and MFCC4 were the most important features in acoustically detecting the ILT vaccine reaction. The fitted Gaussian Naive Bayes model allowed us to evaluate the treatment group to determine if the vocalizations after vaccine administration were detected as non-reactions, due to the anti-inflammatory product’s effectiveness. Of the sample, 99% of vocalizations were classified as non-reactions, due to the anti-inflammatory properties of the product, which reduced vaccine reactions and side effects. The non-invasive detection of hens’ responses to vaccination to prevent respiratory problems in hens described in this paper is an innovative method of measuring and detecting avian welfare.

Domain-Specific Dictionary between Human and Machine Languages

In the realm of artificial intelligence, knowledge graphs have become an effective area of research. Relationships between entities are depicted through a structural framework in knowledge graphs. In this paper, we propose to build a domain-specific medicine dictionary (DSMD) based on the principles of knowledge graphs. Our dictionary is composed of structured triples, where each entity is defined as a concept, and these concepts are interconnected through relationships. This comprehensive dictionary boasts more than 348,000 triples, encompassing over 20,000 medicine brands and 1500 generic medicines. It presents an innovative method of storing and accessing medical data. Our dictionary facilitates various functionalities, including medicine brand information extraction, brand-specific queries, and queries involving two words or question answering. We anticipate that our dictionary will serve a broad spectrum of users, catering to both human users, such as a diverse range of healthcare professionals, and AI applications.

Precision in wheat flour classification: Harnessing the power of deep learning and two-dimensional correlation spectrum (2DCOS)

Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduces an innovative method for wheat flour recognition, combining deep learning (DL) with Two-dimensional correlation spectrum (2DCOS). In this investigation, 316 samples from four distinct types of wheat flour were collected using a near-infrared (NIR) spectrometer, and the raw spectra of each sample underwent preprocessing employing diverse methods. The discrete generalized 2DCOS algorithm was applied to generate 3792 2DCOS images from the preprocessed spectral data. We trained a deep learning model tailored for flour 2DCOS images – EfficientNet. Ultimately, this DL model achieved 100% accuracy in identifying wheat flour within the test set. The findings demonstrate the viability of directly transforming spectra into two-dimensional images for species recognition using 2DCOS and DL. Compared to the traditional stoichiometric method Partial Least Squares Discriminant Analysis (PLS_DA), machine learning methods Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and deep learning methods one-dimensional convolutional neural network (1DCNN) and residual neural network (ResNet), the model proposed in this paper is better suited for wheat flour identification, boasting the highest accuracy. This study offers a fresh perspective on wheat flour type identification and successfully integrates the latest advancements in deep learning with 2DCOS for spectral type identification. Furthermore, this approach can be extended to the spectral identification of other products, presenting a novel avenue for future research in the field.

Real-world implementation of a cloud-based MPC for HVAC control in educational buildings

In their quest to enhance energy efficiency and carbon footprint management, many enterprises are turning their attention towards optimizing their facilities through amplified data monitoring and innovative control methods. In this context, this research highlights a practical solution in a commercial building located in the United States. It explains the creation, implementation, and costs of a universally applicable model predictive control (MPC) framework for the building’s heating, ventilation, and air conditioning (HVAC) system. The paper elaborates on both the hardware and software used to accumulate pertinent data and the formulation of the MPC system. This approach leverages cloud-based microservices and can be seamlessly integrated into current building management systems. In this regard, this paper presents three innovative strategies: Proportional–integral (PI)+Preheating, MPC, and occupancy-based control. These scenarios were designed to improve energy efficiency and thermal comfort significantly by reducing temperature fluctuations and adhering more closely to the setpoint temperature. The implementation of these strategies resulted in substantial energy savings, with reductions in energy consumption of 12.83%, 19.21%, and 14.98%, respectively.

Multi-view graph pooling with coarsened graph disentanglement

Multi-view graph pooling utilizes information from multiple perspectives to generate a coarsened graph, exhibiting superior performance in graph-level tasks. However, existing methods mainly focus on the types of multi-view information to improve graph pooling operations, lacking explicit control over the pooling process and theoretical analysis of the relationships between views. In this paper, we rethink the current paradigm of multi-view graph pooling from an information theory perspective, subsequently introducing GDMGP, an innovative method for multi-view graph pooling derived from the principles of graph disentanglement. This approach effectively simplifies the original graph into a more structured, disentangled coarsened graph, enhancing the clarity and utility of the graph representation. Our approach begins with the design of a novel view mapper that dynamically integrates the node and topology information of the original graph. This integration enhances its information sufficiency. Next, we introduce a view fusion mechanism based on conditional entropy to accurately regulate the task-relevant information in the views, aiming to minimize information loss in the pooling process. Finally, to further enhance the expressiveness of the coarsened graph, we disentangle the fused view into task-relevant and task-irrelevant subgraphs through mutual information minimization, retaining the task-relevant subgraph for downstream tasks. We theoretically demonstrate that the performance of the coarsened graph generated by our GDMGP is superior to that of any single input view. The effectiveness of GDMGP is further validated by experimental results on seven public datasets.

Modeling and Simulation of Hydroxyapatite Recovery in the Desliming Circuit of the Tapira Industrial Plant, Brazil

The modeling and simulation of industrial mineral processing operations are traditionally used for cyclone sizing and optimizations of industrial operations. However, the main models used are based on the total population of particles in the pulp, thus not distinguishing the individual minerals. This article presents the results of an innovative method that investigated the optimization of the metallurgical recovery of P2O5 in the desliming circuit of a phosphate ore processing plant in Brazil. A survey campaign was carried out in the existing industrial circuit, followed by determining the partition curves for the overall particles and specifically for the hydroxyapatite particles. The results were used to calibrate the Narasimha–Mainza cyclone model. From a Base Case determined with reference to the industrial survey, three optimization scenarios were simulated through cyclone geometries and respective operating conditions changes. Simulated scenarios indicated the possibility of P2O5 metallurgical recovery increasing from 9.4% to 12.7% compared to the Base Case.

Cultivation Of Spirulina Platensis for carbon dioxide bio sequestration in hybrid photobioreactor with real-time monitoring system

Innovative methods for effectively sequestering significant amounts of carbon dioxide (CO2) are required to address the ongoing problem of greenhouse gas emissions, which have increased in tandem with industrial development. Optimisation of the performance of the photobioreactor remains a challenge, despite the potential of using photobioreactors and cyanobacteria for bio sequestration. A potential solution lies in the development of a hybrid photobioreactor design. Thus, this study aims to develop a hybrid design of the photobioreactor equipped with tailor-made real-time monitoring system known as FCB2022 for cultivating Spirulina platensis. The performance is evaluated by monitoring real-time data such as pH, dissolved oxygen (DO), and temperature, alongside assessing the growth kinetic of cyanobacterium, CO2 sequestration, oxygen release rate and mass carbon balance. These assessments are conducted under varying photon flux densities (PFD) of 100µmol/ (m2.s), 200µmol/ (m2.s), and 300µmol/ (m2.s). The result demonstrates that FCB2022 exhibited high hydrodynamic performance with parameters like mixing time, circulation time, Reynold number, and empty bed residence time showing favourable values. The optimal ranges for temperature, pH, and dissolved oxygen are determined to be between 23.56 and 29.11 ⁰C, 9.03–9.58, and 6.57–6.89 mgO2/L, respectively. Under the conditions of 15% CO2 and PFD of 300µmol/ (m2.s), the specific growth rates reach 0.36±0.004 /day and maximum biomass concentration attains 1.52±0.03 kg/m3. Notably, the PFD of 300µmol/ (m2.s) yields the highest conversion of carbon into biomass, ranging from 1.09×10−02 kg to 1.26×10−02 kg, representing the yield of 31–83% in each CO2 injection treatment.

Swimming behavior as a potential metric to detect satiation levels of European seabass in marine cages

Aquaculture is anticipated to contribute to two-thirds of the world’s fish consumption by 2030, emphasizing the need for innovative methods to optimize practices for economic viability, social responsibility, and environmental sustainability. Feeding practices play a pivotal role in aquaculture success and the feeding requirements are dynamic, influenced by factors like fish size, environmental conditions, and health status necessitating ongoing improvements in feeding practices. This study addresses a critical gap in feeding control systems in sea cages. It introduces a continuous, real-time monitoring system for analyzing the feeding behavior of European seabass, employing advanced AI models (YOLO and DEEPSORT) and computer vision techniques. The investigation focuses on key parameters, including speed and the newly defined feeding behavior index (FBI), to evaluate swimming responses under varying feeding scenarios exploring meal frequency, feeding time, and feeding quantity. The findings reveal a sensitivity of fish speed and the feeding behavior index (FBI) to different feeding scenarios, elucidating distinct behavioral patterns in response to varying frequencies, times, and quantities of feeding, such as increased activity in the morning relative to later times and the emergence of asymmetric activity patterns when fish are underfed or overfed. Notably, this study is one of the few in the field, presenting the development of a continuous, real-time monitoring system for feeding control in sea cages. Simultaneously, it explores reference curves and threshold values to enhance the overall efficacy of feeding control measures.

Sweet Pepper Leaf Area Estimation Using Semantic 3D Point Clouds Based on Semantic Segmentation Neural Network

In the field of agriculture, measuring the leaf area is crucial for the management of crops. Various techniques exist for this measurement, ranging from direct to indirect approaches and destructive to non-destructive techniques. The non-destructive approach is favored because it preserves the plant's integrity. Among these, several methods utilize leaf dimensions, such as width and length, to estimate leaf areas based on specific models that consider the unique shapes of leaves. Although this approach does not damage plants, it is labor-intensive, requiring manual measurements of leaf dimensions. In contrast, some indirect non-destructive techniques leveraging convolutional neural networks can predict leaf areas more swiftly and autonomously. In this paper, we propose a new direct method using 3D point clouds constructed by semantic RGB-D (Red Green Blue and Depth) images generated by a semantic segmentation neural network and RGB-D images. The key idea is that the leaf area is quantified by the count of points depicting the leaves. This method demonstrates high accuracy, with an R2 value of 0.98 and a RMSE (Root Mean Square Error) value of 3.05 cm2. Here, the neural network's role is to segregate leaves from other plant parts to accurately measure the leaf area represented by the point clouds, rather than predicting the total leaf area of the plant. This method is direct, precise, and non-invasive to sweet pepper plants, offering easy leaf area calculation. It can be implemented on laptops for manual use or integrated into robots for automated periodic leaf area assessments. This innovative method holds promise for advancing our understanding of plant responses to environmental changes. We verified the method's reliability and superior performance through experiments on individual leaves and whole plants.

Intermolecular Interaction Between BODIPY and TPE Enhances Phototherapy

AbstractFluoroborodipyrrole (BODIPY) and Tetraphenylethylene (TPE) are well‐known molecules in the realm of optical materials, celebrated for their exceptional properties. Recent research increasingly focuses on their combined use. In this study, a novel non‐synthetic method is taken to harness the synergistic therapeutic potential stemming from intermolecular interactions by employing nanoparticle encapsulation and core–shell structure. In vitro and in vivo experiments results show that the nanoparticles encapsulating BODIPY and TPE exhibit low cytotoxicity, efficient antitumor properties, and excellent visualization, achieving remarkable 1+1>2 effects. This innovative method not only provides high therapeutic efficacy, but also reduces time and economic costs, providing a novel perspective for exploring the combination of BODIPY and TPE as well as similar molecules in the field of optical materials.